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Record W2801984431 · doi:10.1139/tcsme-2017-0019

Kinematic analysis of a newly designed cable-driven manipulator

2018· article· en· W2801984431 on OpenAlexvenueno aff
Wei Xu, Yaoyao Wang, Surong Jiang, Jiafeng Yao, Bai Chen

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicRobotic Mechanisms and Dynamics
Canadian institutionsnot available
FundersGovernment of Jiangsu ProvinceNatural Science Foundation of Jiangsu ProvinceNational Natural Science Foundation of China
KeywordsPulleyKinematicsCoupling (piping)ActuatorUniversal jointCartesian coordinate systemWorkspaceControl theory (sociology)Motion (physics)Orientation (vector space)Computer scienceEngineeringTopology (electrical circuits)RobotStructural engineeringMechanical engineeringGeometryPhysicsMathematicsClassical mechanics

Abstract

fetched live from OpenAlex

In this paper, the cable routing configurations for a cable-driven manipulator are introduced, and the impact of motion coupling caused by cable transmission routing of a 2n type cable-driven manipulator is analyzed in detail. Based on different configurations of cable routings, the relationship between variation of joint angles and the geometrical sizes of guide pulleys is established, represented by a matrix for coupled motion. Moreover, based on the effects of the motion coupling of a cable-driven manipulator, we propose the condition for the invariance of orientation, which can be achieved constraining of the geometrical sizes of guide pulleys and driven wheels. In addition, to identify the correctness of analysis for coupled motion, a 3-DOFs planer cable-driven manipulator prototyping model is constructed, and the kinematics and trajectory planning has been solved. Finally, the relationship among actuator space, joint space, and Cartesian space, including the mapping of the motion coupling, is experimentally validated. The property of invariance of orientation is also validated by an experiment.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.728
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.012
GPT teacher head0.202
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations14
Published2018
Admission routes1
Has abstractyes

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